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Feed Ranking System Design

Explore how to design feed ranking systems that handle billions of samples and terabytes of data. Understand feature stores, model inference flows, and scaling strategies to build efficient personalized recommendation systems. This lesson guides you through practical considerations for system design in real-world ML applications.

4. Calculation & estimation

Assumptions

  • 300 million monthly active users
  • On average, a user sees 40 activities per visit. Each user visits 10 times per month.
  • We have 12 * 101010^{10} or 120 billion observations/samples.
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